VERTIGo: A Visual Platform for Querying and Exploring Large Multilayer Networks.

Many real world data can be modeled by a graph with a set of nodes interconnected to each other by multiple relationships. Such a rich graph is called multilayer graph or network. Providing useful visualization tools to support the query process for such graphs is challenging.

Assessment of congruence between co-occurrence and functional networks: A new framework for revealing community assembly rules.

Describing how communities change over space and time is crucial to better understand and predict the functioning of ecosystems. We propose a new methodological framework, based on network theory and modularity concept, to determine which type of mechanisms (i.e.

Enhancing Graph-Based Semisupervised Learning via Knowledge-Aware Data Embedding.

Semisupervised learning (SSL) is a family of classification methods conceived to reduce the amount of required labeled information in the training phase. Graph-based methods are among the most popular semisupervised strategies: the nearest neighbor graph is built in such a way that the manifold of the data is captured and the labeled information is propagated to target samples along the structure of the manifold. Research in graph-based SSL has mainly focused on two aspects: 1) the construction of the k -nearest neighbors graph and/or 2) the propagation algorithm providing the classification.

A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data.

In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances.